SOTAVerified

Relational Reasoning

The goal of Relational Reasoning is to figure out the relationships among different entities, such as image pixels, words or sentences, human skeletons or interactive moving agents.

Source: Social-WaGDAT: Interaction-aware Trajectory Prediction via Wasserstein Graph Double-Attention Network

Papers

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Showing 181–190 of 483 papers

TitleStatusHype
Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization—0
Coresets for Relational Data and The ApplicationsCode0
Swift Markov Logic for Probabilistic Reasoning on Knowledge Graphs—0
Differentiable Parsing and Visual Grounding of Natural Language Instructions for Object Placement—0
Decoupling Mixture-of-Graphs: Unseen Relational Learning for Knowledge Graph Completion by Fusing Ontology and Textual Experts—0
Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL—0
Structured Knowledge Grounding for Question Answering—0
VGStore: A Multimodal Extension to SPARQL for Querying RDF Scene GraphCode0
Hierarchical Relational Learning for Few-Shot Knowledge Graph Completion—0
Graph Convolutional Networks from the Perspective of Sheaves and the Neural Tangent Kernel—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CTP A4 Hops0.99—Unverified